Image compression refers to techniques for reducing the bit size of digital image files. Reducing the bit size of digital images provides benefits relating to both file storage and file transmission. For example, a web page containing image files can be transmitted across a network and downloaded by an end-user device more quickly if such image files are of reduced file size. Generally, image compression techniques work to compress images by eliminating irrelevant or redundant data and transforming image data to more efficiently convey the image information.
Image compression techniques generally fall within two categories: lossless and lossy. Lossless compression techniques reduce the bit size of a digital image without “losing” any of the information. In other words, lossless compression techniques can be reversed such that the exact original data can be reconstructed from the compressed data. On the other hand, lossy compression techniques compress data by discarding or “losing” some of it. Once the data is discarded, it is generally impossible to return to the exact original.
Thus, lossless compression techniques are generally preferred when it is important that the original and the decompressed data be identical. Typical examples include executable programs, text documents, or, with respect to image compression, medical files or technical drawings. However, because lossless compression techniques are constrained by the requirement that original data be exactly reconstructed, the resulting compression gains are limited.
On the contrary, because lossy techniques are not subject to such constraints, lossy techniques are able to achieve greater magnitudes of compression gains. Thus, lossy techniques are more commonly used when a minimal amount of quality degradation is acceptable and outweighed by the potential benefits associated with reduced bit size. For example, images and other multimedia data, especially in applications such as streaming media, are commonly compressed using lossy techniques.
Near-lossless compression techniques allow some of the original data to be lost. However, the overall loss of data is controlled to remain within a bounded reconstruction error. Therefore, near-lossless compression techniques can represent a compromise between the compression gains of lossy techniques and the quality assurance of lossless techniques.
Thus, improved near-lossless techniques that provide increased compression gains while remaining within a bounded error are desirable.